As of 03 January 2021, the WHO African region is the least affected by the coronavirus disease-2019 (COVID-19) pandemic, accounting for only 2.4% of cases and deaths reported globally. However, concerns abound about whether the number of cases and deaths reported from the region reflect the true burden of the disease and how the monitoring of the pandemic trajectory can inform response measures.

We retrospectively estimated four key epidemiological parameters (the total number of cases, the number of missed cases, the detection rate and the cumulative incidence) using the COVID-19 prevalence calculator tool developed by Resolve to Save Lives. We used cumulative cases and deaths reported during the period 25 February to 31 December 2020 for each WHO Member State in the region as well as population data to estimate the four parameters of interest. The estimated number of confirmed cases in 42 countries out of 47 of the WHO African region included in this study was 13 947 631 [95% confidence interval (CI): 13 334 620–14 635 502] against 1 889 512 cases reported, representing 13.5% of overall detection rate (range: 4.2% in Chad, 43.9% in Guinea). The cumulative incidence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was estimated at 1.38% (95% CI: 1.31%–1.44%), with South Africa the highest [14.5% (95% CI: 13.9%–15.2%)] and Mauritius [0.1% (95% CI: 0.099%–0.11%)] the lowest. The low detection rate found in most countries of the WHO African region suggests the need to strengthen SARS-CoV-2 testing capacities and adjusting testing strategies.

The effectiveness of screening travellers during times of international disease outbreak is contentious, especially as the reduction in the risk of disease importation can be very small. Border screening typically consists of travellers being thermally scanned for signs of fever and/or completing a survey declaring any possible symptoms prior to admission to their destination country; while more thorough testing typically exists, these would generally prove more disruptive to deploy. In this paper, we describe a simple Monte Carlo based model that incorporates the epidemiology of coronavirus disease-2019 (COVID-19) to investigate the potential decrease in risk of disease importation that might be achieved by requiring travellers to undergo screening upon arrival during the current pandemic. This is a purely theoretical study to investigate the maximum impact that might be attained by deploying a test or testing programme simply at the point of entry, through which we may assess such action in the real world as a method of decreasing the risk of importation. We, therefore, assume ideal conditions such as 100% compliance among travellers and the use of a ‘perfect’ test. In addition to COVID-19, we also apply the presented model to simulated outbreaks of influenza, severe acute respiratory syndrome (SARS) and Ebola for comparison. Our model only considers screening implemented at airports, being the predominant method of international travel. Primary results showed that in the best-case scenario, screening at the point of entry may detect a maximum of 8.8% of travellers infected with COVID-19, compared to 34.8.%, 9.7% and 3.0% for travellers infected with influenza, SARS and Ebola respectively. While results appear to indicate that screening is more effective at preventing disease ingress when the disease in question has a shorter average incubation period, our results suggest that screening at the point of entry alone does not represent a sufficient method to adequately protect a nation from the importation of COVID-19 cases.

The SARS-CoV-2 virus is rapidly evolving via mutagenesis, lengthening the pandemic, and threatening the public health. Until August 2021, 12 variants of SARS-CoV-2 named as variants of concern (VOC; Alpha to Delta) or variants of interest (VOI; Epsilon to Mu), with significant impact on transmissibility, morbidity, possible reinfection and mortality, have been identified. The VOC Delta (B.1.617.2) of Indian origin is now the dominant and the most contagious variant worldwide as it provokes a strong binding to the human ACE2 receptor, increases transmissibility and manifests considerable immune escape strategies after natural infection or vaccination. Although the development and administration of SARS-CoV-2 vaccines, based on different technologies (mRNA, adenovirus carrier, recombinant protein, etc.), are very promising for the control of the pandemic, their effectiveness and neutralizing activity against VOCs varies significantly. In this review, we describe the most significant circulating variants of SARS-CoV-2, and the known effectiveness of currently available vaccines against them.

The use of a Kaplan–Meier (K–M) survival time approach is generally considered appropriate to report antimalarial efficacy trials. However, when a treatment arm has 100% efficacy, confidence intervals may not be computed. Furthermore, methods that use probability rules to handle missing data for instance by multiple imputation, encounter perfect prediction problem when a treatment arm has full efficacy, in which case all imputed values are either treatment success or all imputed values are failures. The use of a survival K–M method addresses this imputation problem in estimating the efficacy estimates also referred to as cure rates. We discuss the statistical challenges and propose a potential way forward.

The proposed approach includes the use of K–M estimates as the main measure of efficacy. Confidence intervals could be computed using the binomial exact method. p-Values for comparison of difference in efficacy between treatments can be estimated using Fisher’s exact test. We emphasize that when efficacy rates are not 100% in both groups, the K–M approach remains the main strategy of analysis considering its statistical robustness in handling missing data and confidence intervals can be computed under such scenarios.

Ex post moral hazard arises when the insured has an unobservable influence on the size of a loss after its occurrence. In automobile (property) insurance, ex post moral hazard could increase in the scope of the repairs and/or the value of the repairs. Both vehicle owners and auto repairers could gain from increasing the scope of repairs, while auto repairers would gain from an increase in the value of repairs. An analysis of 994 Australian road traffic crashes found that ex post moral hazard increased the value of repairs by 46.8 per cent of which 9 percentage points was explained by an increase in the scope of the repairs, which was defined as an increased from 2 to 2.4 parts per auto repair.

In this paper, we present a method for generating a copula by composing two arbitrary n-dimensional copulas via a vector of bivariate functions, where the resulting copula is named as the multivariate composite copula. A necessary and sufficient condition on the vector guaranteeing the composite function to be a copula is given, and a general approach to construct the vector satisfying this necessary and sufficient condition via bivariate copulas is provided. The multivariate composite copula proposes a new framework for the construction of flexible multivariate copula from existing ones, and it also includes some known classes of copulas. It is shown that the multivariate composite copula has a clear probability structure, and it satisfies the characteristic of uniform convergence as well as the reproduction property for its component copulas. Some properties of multivariate composite copulas are discussed. Finally, numerical illustrations and an empirical example on financial data are provided to show the advantages of the multivariate composite copula, especially in capturing the tail dependence.

This study was endeavoured to contribute in furthering our understanding of the molecular epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by sequencing and analysing the first full-length genome sequences obtained from 48 coronavirus disease-2019 (COVID-19) patients in five districts in Western Serbia in the period April 2020–July 2020. SARS-CoV-2 sequences in Western Serbia distinguished from the Wuhan sequence in 128 SNPs in total. The phylogenetic structure of local SARS-CoV-2 isolates suggested the existence of at least four distinct groups of SARS-CoV-2 strains in Western Serbia. The first group is the most similar to the strain from Italy. These isolates included two 20A sequences and 15−30 20B sequences that displayed a newly occurring set of four conjoined mutations. The second group is the most similar to the strain from France, carrying two mutations and belonged to 20A clade. The third group is the most similar to the strain from Switzerland carrying four co-occurring mutations and belonging to 20B clade. The fourth group is the most similar to another strain from France, displaying one mutation that gave rise to a single local isolate that belonged to 20A clade.

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)14 model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

In June 2020, a large-scale food poisoning outbreak involving about 3000 elementary and junior high school students occurred in Yashio, Saitama, Japan. A school lunch was the only food stuff ingested by all of the patients. Escherichia coli serotype O7:H4 carrying the astA gene for enteroaggregative E. coli (EAggEC) heat-stable enterotoxin 1 (EAST1) was detected in faecal specimens from the patients, and sample inspection revealed its presence in a seaweed salad and red seaweed (Gigartina tenella) as one of the raw materials. Analysis of the antibiotic sensitivity of the isolates revealed resistance to ampicillin and cefotaxime. All isolates were confirmed to be of the same origin by pulsed-field gel electrophoresis after digestion with the restriction enzyme XbaI, and single nucleotide polymorphism analysis using whole genome sequencing. To our knowledge, this is the first report of a large-scale food poisoning caused by E. coli O7:H4, which lacks well-characterized virulence genes other than astA.

The article aims to estimate and forecast the transmissibility of shigellosis and explore the association of meteorological factors with shigellosis. The mathematical model named Susceptible–Exposed–Symptomatic/Asymptomatic–Recovered–Water/Food (SEIARW) was used to explore the feature of shigellosis transmission based on the data of Wuhan City, China, from 2005 to 2017. The study applied effective reproduction number (Reff) to estimate the transmissibility. Daily meteorological data from 2008 to 2017 were used to determine Spearman's correlation with reported new cases and Reff. The SEIARW model fit the data well (χ2 = 0.00046, p > 0.999). The simulation results showed that the reservoir-to-person transmission of the shigellosis route has been interrupted. The Reff would be reduced to a transmission threshold of 1.00 (95% confidence interval (CI) 0.82–1.19) in 2035. Reducing the infectious period to 11.25 days would also decrease the value of Reff to 0.99. There was a significant correlation between new cases of shigellosis and atmospheric pressure, temperature, wind speed and sun hours per day. The correlation coefficients, although statistically significant, were very low (<0.3). In Wuhan, China, the main transmission pattern of shigellosis is person-to-person. Meteorological factors, especially daily atmospheric pressure and temperature, may influence the epidemic of shigellosis.

Several candidates of universal influenza vaccine (UIV) have entered phase III clinical trials, which are expected to improve the willingness and coverage of the population substantially. The impact of UIV on the seasonal influenza epidemic in low influenza vaccination coverage regions like China remains unclear. We proposed a new compartmental model involving the transmission of different influenza subtypes to evaluate the effects of UIV. We calibrated the model by weekly surveillance data of influenza in Xi'an City, Shaanxi Province, China, during 2010/11–2018/19 influenza seasons. We calculated the percentage of averted infections under 2-month (September to October) and 6-month (September to the next February) vaccination patterns with varied UIV effectiveness and coverage in each influenza season, compared with no UIV scenario. A total of 195 766 influenza-like illness (ILI) cases were reported during the nine influenza seasons (2010/11–2018/19), of which the highest ILI cases were among age group 0–4 (59.60%) years old, followed by 5–14 (25.22%), 25–59 (8.19%), 15–24 (3.75%) and ⩾60 (3.37%) years old. The influenza-positive rate for all age groups among ILI cases was 17.51%, which is highest among 5–14 (23.75%) age group and followed by 25–59 (16.44%), 15–24 (16.42%), 0–4 (14.66%) and ⩾60 (13.98%) age groups, respectively. Our model showed that UIV might greatly avert influenza infections irrespective of subtypes in each influenza season. For example, in the 2018/19 influenza season, 2-month vaccination pattern with low UIV effectiveness (50%) and coverage (10%), and high UIV effectiveness (75%) and coverage (30%) could avert 41.6% (95% CI 27.8–55.4%) and 83.4% (80.9–85.9%) of influenza infections, respectively; 6-month vaccination pattern with low and high UIV effectiveness and coverage could avert 32.0% (15.9–48.2%) and 74.2% (69.7–78.7%) of influenza infections, respectively. It would need 11.4% (7.9–15.0%) of coverage to reduce half of the influenza infections for 2-month vaccination pattern with low UIV effectiveness and 8.5% (5.0–11.2%) of coverage with high UIV effectiveness, while it would need 15.5% (8.9–20.7%) of coverage for 6-month vaccination pattern with low UIV effectiveness and 11.2% (6.5–15.0%) of coverage with high UIV effectiveness. We conclude that UIV could significantly reduce the influenza infections even for low UIV effectiveness and coverage. The 2-month vaccination pattern could avert more influenza infections than the 6-month vaccination pattern irrespective of influenza subtype and UIV effectiveness and coverage.

With increasing demand for large numbers of testing during the coronavirus disease 2019 pandemic, alternative protocols were developed with shortened turn-around time. We evaluated the performance of such a protocol wherein 1138 consecutive clinic attendees were enrolled; 584 and 554 respectively from two independent study sites in the cities of Pune and Kolkata. Paired nasopharyngeal and oropharyngeal swabs were tested by using both reference and index methods in a blinded fashion. Prior to conducting real-time polymerase chain reaction, swabs collected in viral transport medium (VTM) were processed for RNA extraction (reference method) and swabs collected in a dry tube without VTM were incubated in Tris–EDTA–proteinase K buffer for 30 min and heat-inactivated at 98 °C for 6 min (index method). Overall sensitivity and specificity of the index method were 78.9% (95% confidence interval (CI) 71–86) and 99% (95% CI 98–99.6), respectively. Agreement between the index and reference method was 96.8% (k = 0.83, s.e. = 0.03). The reference method exhibited an enhanced detection of viral genes (E, N and RNA-dependent RNA polymerase) with lower Ct values compared to the index method. The index method can be used for detecting severe acute respiratory syndrome corona virus-2 infection with an appropriately chosen primer–probe set and heat treatment approach in pressing time; low sensitivity constrains its potential wider use.

Little is known about the decision-making process of college students in Lebanon regarding coronavirus disease-2019 (COVID-19) vaccination. The aim of this study was to identify factors predicting behavioural intentions of students enrolled at the American University of Beirut to obtain a COVID-19 vaccine. A total of 3805 students were randomly selected. Participants were divided into three groups: vaccine accepting (willing to take or already took the vaccine), vaccine hesitant (hesitant to take the vaccine) and vaccine resistant (decided not to take the vaccine). Overall, participants were vaccine accepting (87%), with 10% and 3% being hesitant and resistant, respectively. Vaccine hesitancy was significantly associated with nationality, residency status and university rank. Participants who believed the vaccine was safe and in agreement with their personal views were less likely to be hesitant. Participants who did not receive the flu vaccine were more hesitant than those who did. Moreover, a significant association between hesitancy and agreement with conspiracies was observed. A high level of knowledge about COVID-19 disease and vaccine resulted in lower odds of vaccine resistance among students. The factors identified explaining each of the three vaccine intention groups can be used as core content for health communication and social marketing campaigns to increase the rate of COVID-19 vaccination.

Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $, a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an “unknown and rapidly mutating” virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people.

Poultry contact is a risk factor for zoonotic transmission of non-typhoidal Salmonella spp. Salmonella illness outbreaks in the United States are identified by PulseNet, the national laboratory network for enteric disease surveillance. During 2020, PulseNet observed a 25% decline in the number of Salmonella clinical isolates uploaded by state and local health departments. However, 1722 outbreak-associated Salmonella illnesses resulting from 12 Salmonella serotypes were linked to contact with privately owned poultry, an increase from all previous years. This report highlights the need for continued efforts to prevent backyard poultry-associated outbreaks of Salmonella as ownership increases in the United States.

The corona virus disease-2019 (COVID-19) pandemic began in Wuhan, China, and quickly spread around the world. The pandemic overlapped with two consecutive influenza seasons (2019/2020 and 2020/2021). This provided the opportunity to study community circulation of influenza viruses and severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in outpatients with acute respiratory infections during these two seasons within the Bavarian Influenza Sentinel (BIS) in Bavaria, Germany. From September to March, oropharyngeal swabs collected at BIS were analysed for influenza viruses and SARS-CoV-2 by real-time polymerase chain reaction. In BIS 2019/2020, 1376 swabs were tested for influenza viruses. The average positive rate was 37.6%, with a maximum of over 60% (in January). The predominant influenza viruses were Influenza A(H1N1)pdm09 (n = 202), Influenza A(H3N2) (n = 144) and Influenza B Victoria lineage (n = 129). In all, 610 of these BIS swabs contained sufficient material to retrospectively test for SARS-CoV-2. SARS-CoV-2 RNA was not detectable in any of these swabs. In BIS 2020/2021, 470 swabs were tested for influenza viruses and 457 for SARS-CoV-2. Only three swabs (0.6%) were positive for Influenza, while SARS-CoV-2 was found in 30 swabs (6.6%). We showed that no circulation of SARS-CoV-2 was detectable in BIS during the 2019/2020 influenza season, while virtually no influenza viruses were found in BIS 2020/2021 during the COVID-19 pandemic.

School lockdowns have been widely used to control the COVID-19 pandemic. However, these lockdowns may have a significant negative impact on the lives of young people. In this study, we have evaluated the impact of closing lower secondary schools for COVID-19 incidence in 13–15-year-olds in Finland, in a situation where restrictions and recommendation of social distancing were implemented uniformly in the entire country. COVID-19 case numbers were obtained from the National Infectious Disease Registry (NIDR) of the Finnish Institute for Health and Welfare, in which clinical microbiology laboratories report all positive SARS-CoV-2 tests with unique identifiers in a timely manner. The NIDR is linked to population data registry, enabling calculation of incidences. We estimated the differences in trends between areas with both restaurant and lower secondary school closures and areas with only restaurant closures in different age groups by using joinpoint regression. We also estimated the differences in trends between age groups. Based on our analysis, closing lower secondary schools had no impact on COVID-19 incidence among 13–15-year-olds. No significant changes on COVID-19 incidence were observed in other age groups either.